CN110164137A - Based on bayonet to the recognition methods of the fake license plate vehicle of running time and system, medium - Google Patents
Based on bayonet to the recognition methods of the fake license plate vehicle of running time and system, medium Download PDFInfo
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- CN110164137A CN110164137A CN201910412526.3A CN201910412526A CN110164137A CN 110164137 A CN110164137 A CN 110164137A CN 201910412526 A CN201910412526 A CN 201910412526A CN 110164137 A CN110164137 A CN 110164137A
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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Abstract
The invention discloses a kind of based on bayonet to the recognition methods of the fake license plate vehicle of running time and system, a kind of recognition methods and system, computer-readable storage medium of the fake license plate vehicle carrying out deep learning to running time based on bayonet.Recognition methods and system based on bayonet to the fake license plate vehicle of running time of the invention, it is for statistical analysis by running bayonet track data to vehicle, data modeling, with good recognition accuracy, especially combine the register information of number plate vehicle, strong improve checks and detains fake-licensed car accuracy and efficiency, beautifies vehicles operation environment, ensures people's life and property safety.In addition, it is not necessary that obtaining the continuous feature of track of vehicle in the case where road network, range data, while it can be also compatible with leakage beat of data, compared by calculating distance or determining that track continuity effect is more preferable according to road network, complexity is lower.
Description
Technical field
The present invention relates to field of intelligent transportation technology, particularly, are related to a kind of fake-licensed car based on bayonet to running time
Recognition methods and system, computer-readable storage medium.
Background technique
Fake license plate vehicle is commonly called as clone's vehicle, refers to model and color referring to true board vehicle, the identical false-trademark of number is covered same
On template number and the vehicle of color.Fake-licensed car main cause is to apply others' licence plate, so on the way reckless when driving, is taken the post as
Meaning such as runs a red light, rolls yellow line at the behavior of serious traffic violation, has brought serious security risk, Huo Zhejia to traffic safety
In front yard, mutual deck in circle of friends, to evade coherence check and expense.Since fake license plate vehicle concealment is strong, it is difficult to investigate and collect evidence
The features such as, and a kind of effective method or system there is no to realize the automatic identification to it at present, therefore how to accurately identify fake-licensed car
Yun Guan authorities urgent problem is had become, becomes the most important thing of control fake license plate vehicle.
Summary of the invention
The present invention provides it is a kind of can to the recognition methods of the fake license plate vehicle of running time and system, computer based on bayonet
The storage medium of reading, to solve the technical issues of prior art can not accurately identify fake license plate vehicle.
According to an aspect of the present invention, a kind of recognition methods based on bayonet to the fake license plate vehicle of running time is provided,
The following steps are included:
Step S1: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S2: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the card of n*n to combination
Mouth is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S3: the running track of each car is obtained, and the bayonet track data of each car is successive according to running time
All bayonets sequence that sequence is passed through, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTIn before
The two bayonets composition bayonet passed through afterwards is to obtain number plate bayonet to track sets Kd, then from number plate bayonet to track sets Kd
The middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track sets KT
Adjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sets Ka；
Step S4: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S5: bayonet is established to running time threshold to running time minimum threshold to running time and bayonet based on bayonet
It is worth two-dimensional matrix Kn；
Step S6: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S7: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet occurs 0 probability P to running time
And normal distribution is applied, the boundary P value of ± 3 σ of u is obtained, the number plate except ± 3 section σ u is fake-licensed car；Within ± 3 section σ u and
Number plate near the P value of boundary is doubtful fake-licensed car.
Further, the step S4 is specially
According to the vehicle pass-through bayonet in step S3 to record, by vehicle by bayonet to (ki, kj) in bayonet kiTime
It is denoted as tki, by bayonet kjTime be denoted as tkj, then bayonet is to (ki, kj) journey time be tij=tkj- tki；
To bayonet to (ki, kj) all running time tijUsing normal distribution, the t of u-3 σ is takenijAs bayonet kiTo card
Mouth kjMinimum time threshold value t (ki, kj)；Or when by outlier algorithm or clustering algorithm finding the driving of each bayonet pair
Between tijOutlier threshold value, outlier threshold value is bayonet kiTo bayonet kjMinimum time threshold value t (ki, kj)。
Further, the step S5 specifically:
When initial, by bayonet to two-dimensional matrix KmIn bayonet to (ki, kj) it is assigned a value of 0, then by bayonet to two-dimensional matrix
KmIn corresponding bayonet to (ki, kj) its bayonet is substituted for running time minimum time threshold value t (ki, kj), to obtain bayonet pair
Running time threshold value two-dimensional matrix Kn。
Further, the step S6 specifically:
By the adjacent bayonet of number plate to track sets KaBayonet to being substituted for corresponding bayonet to running time tij, then,
By the adjacent bayonet of number plate to track sets KaIn bayonet to search bayonet to running time threshold value two-dimensional matrix KnIn time
Threshold value, if bayonet is to running time tijLess than KnMiddle time threshold is then set to 0, and otherwise constant, the adjacent bayonet of number plate is to rail
Mark sequence KaBecome bayonet to running time matrix Kb。
Further, the recognition methods of the fake license plate vehicle is further comprising the steps of:
Step S8: the number plate of the fake license plate vehicle of identification and the number plate of doubtful fake license plate vehicle are stored in fake-licensed car database.
Further, the recognition methods of the fake license plate vehicle is further comprising the steps of:
Step S9: fake license plate vehicle is captured；
The step S9 includes:
Step S91: fake-licensed car or doubtful fake-licensed car interior for a period of time bayonet data and bayonet longitude and latitude are obtained, and therefrom
High frequency bayonet and high frequency period are extracted as hot spot bayonet；
Step S92: obtaining number plate of vehicle to vehicle photographic, or be manually entered number plate of vehicle, or access card oral instructions in real time enter
Number plate；And
Step S93: the number plate of vehicle of acquisition is compared with fake-licensed car database in hot spot bayonet, if comparison result is
Fake-licensed car, outputting alarm information immediately, while recalling from special bus pipe data system the information of the affiliated vehicle of the number plate, auxiliary
Traffic police arrests fake license plate vehicle.
The present invention also provides a kind of based on bayonet to the identifying system of the fake license plate vehicle of running time, is suitable for as described above
Recognition methods, including
Data acquisition module carries out the required basic data of fake license plate vehicle identification for obtaining,
Statistical analysis module, for for statistical analysis to vehicle running track data and identify fake license plate vehicle；
Controller, for controlling the data collecting module collected basic data and for controlling the statistical analysis module
It is for statistical analysis and identify fake license plate vehicle；
The controller is connect with the data acquisition module and statistical analysis module respectively, the data acquisition module and
Statistical analysis module connection.
Further, the identifying system further includes
Fake-licensed car captures terminal, connect with the controller, for capturing to fake license plate vehicle；
The fake-licensed car captures terminal
Hot spot bayonet acquiring unit, for obtaining fake-licensed car or doubtful fake-licensed car interior for a period of time bayonet data and bayonet
Longitude and latitude, and high frequency bayonet and high frequency period are therefrom extracted as hot spot bayonet；
Number plate acquisition unit for obtaining number plate of vehicle to vehicle photographic or for being manually entered number plate of vehicle, or is used for
The number plate that real-time access card oral instructions enter；
Capture unit, for the number plate of vehicle of acquisition to be compared with fake-licensed car database in hot spot bayonet, if comparing
It as a result is fake-licensed car, outputting alarm information immediately, while recalling from special bus pipe data system the letter of the affiliated vehicle of the number plate
Breath, auxiliary traffic police arrest fake license plate vehicle.
The present invention also provides a kind of computer-readable storage mediums, for storing based on bayonet to running time to set
The computer program that board vehicle is identified, the computer program execute following steps when running on computers:
Step S1: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S2: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the card of n*n to combination
Mouth is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S3: the running track of each car is obtained, and the bayonet track data of each car is successive according to running time
All bayonets sequence that sequence is passed through, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTIn before
The two bayonets composition bayonet passed through afterwards is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track sequence
Arrange KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track sequence
Arrange KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sets
Ka；
Step S4: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S5: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S6: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S7: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet occurs 0 probability P to running time
And normal distribution is applied, the boundary P value of ± 3 σ of u is obtained, the number plate except ± 3 section σ u is fake-licensed car；Within ± 3 section σ u,
Number plate near the P value of boundary is doubtful fake-licensed car.
The present invention also provides a kind of recognition methods of fake license plate vehicle for carrying out deep learning to running time based on bayonet, packets
Include following steps:
Step S100: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S200: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form n*n's to combination
Bayonet is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S300: the running track of each car is obtained, and by the bayonet track data of each car according to running time elder generation
All bayonets sequence that sequence is passed through afterwards, obtains the number plate bayonet track sets K of each carT, will be in number plate bayonet track sets
Two bayonets composition bayonet that front and back passes through is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track
Sequence KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track
Sequence KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sequence
Arrange Ka；
Step S400: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S500: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S600: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S700: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet is general to running time appearance 0
Rate P simultaneously applies normal distribution, obtains the corresponding P of u valueuValue；And
Step S800: deep learning is carried out to realize intelligent recognition fake license plate vehicle using convolutional neural networks.
Further, the step S800 specifically includes the following steps:
Step S801: selection PuIt is worth the one-dimensional bayonet of x number plates nearby to running time matrix KbAs positive sample E+；
Step S802: by all number plate random combine Cheng Yitai new cars, generating new interim number plate, while merging the two card
Mouth track data, repeats step S300-S700, chooses PuIt is worth the one-dimensional bayonet of x number plates nearby to running time matrix KbAs
Negative sample E-；
Step S803: unified positive sample E+, negative sample E-With one-dimensional bayonet to running time matrix KbLength and returned
One change processing；
Step S804: by positive sample E+With negative sample E-Convolutional neural networks are inputted, carry out feature learning, and utilize
Softmax classifier classifies to the feature that convolutional network learns, and obtains the fake license plate vehicle intelligence based on one-dimensional deep learning
It can identification model RAI, then use verify data, constantly adjustment threshold value, the date, x and x value interval, sample length, convolution
Neural network parameter is trained, and obtains best fake license plate vehicle intelligent recognition model RAI；And
Step S805: best fake license plate vehicle intelligent recognition model R is utilizedAITo the one-dimensional bayonet of all vehicles to running time square
Battle array KbIt is identified, obtains deck number plate set.
The present invention also provides a kind of identifying system of fake license plate vehicle for carrying out deep learning to running time based on bayonet, packets
It includes
Data acquisition module carries out the required basic data of fake license plate vehicle identification for obtaining,
Deep learning module, for carrying out data modeling, statistical analysis, deep learning and knowledge to vehicle running track data
Other fake license plate vehicle；
Controller, for controlling the data collecting module collected basic data and for controlling the deep learning module
Carry out data modeling, statistical analysis, deep learning and identification fake license plate vehicle；
The controller is connect with the data acquisition module and deep learning module respectively, the deep learning module with
Data acquisition module connection.
The present invention also provides a kind of computer-readable storage mediums, are carried out based on bayonet to running time for storing
Computer program of the deep learning to be identified to fake license plate vehicle executes following when the computer program is run on computers
Step:
Step S100: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S200: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form n*n's to combination
Bayonet is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S300: the running track of each car is obtained, and by the bayonet track data of each car according to running time elder generation
All bayonets sequence that sequence is passed through afterwards, obtains the number plate bayonet track sets K of each carT, will be in number plate bayonet track sets
Two bayonets composition bayonet that front and back passes through is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track
Sequence KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track
Sequence KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sequence
Arrange Ka；
Step S400: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S500: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S600: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S700: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet is general to running time appearance 0
Rate P simultaneously applies normal distribution, obtains the corresponding P of u valueuValue；And
Step S800: deep learning is carried out to realize intelligent recognition fake license plate vehicle using convolutional neural networks.
The invention has the following advantages:
Recognition methods based on bayonet to the fake license plate vehicle of running time of the invention, by running bayonet track to vehicle
Data are for statistical analysis, data modeling, have good recognition accuracy, especially combine the register information of number plate vehicle,
Strong improve checks and detains fake-licensed car accuracy and efficiency, beautifies vehicles operation environment, ensures people's life and property safety.In addition,
The continuous feature for obtaining track of vehicle in the case where road network, range data is not needed, while can be compatible with leakage beat of data yet, is compared logical
It crosses calculating distance or determines that track continuity effect is more preferable according to road network, complexity is lower.
Of the invention is equally had the above advantages based on identifying system of the bayonet to the fake license plate vehicle of running time.
The recognition methods of the fake license plate vehicle for carrying out deep learning to running time based on bayonet of the invention, by vehicle
It is for statistical analysis to run bayonet track data, data modeling, and learnt and trained using convolutional neural networks, it uses
Convolutional neural networks, avoid explicit feature extraction, the error introduced in threshold value setting, and implicitly from training data into
Row study；Convolutional neural networks have in terms of image procossing unique superiority, especially one-dimensional vector can directly it is defeated
Enter the complexity that network this feature avoids data reconstruction in feature extraction and assorting process.In addition, it is not necessary that road network, distance
Obtain the continuous feature of track of vehicle in the case where data, while can also be compatible with leakage beat of data, compare by calculate distance or
Determine that track continuity effect is more preferable according to road network, complexity is lower.
Of the invention equally being had based on identifying system of the bayonet to the fake license plate vehicle that running time carries out deep learning is upper
State advantage.
Other than objects, features and advantages described above, there are also other objects, features and advantages by the present invention.
Below with reference to figure, the present invention is described in further detail.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is being shown based on process of the bayonet to the recognition methods of the fake license plate vehicle of running time for the preferred embodiment of the present invention
It is intended to.
Fig. 2 is the sub-process schematic diagram of the step S9 in Fig. 1 of the preferred embodiment of the present invention.
Fig. 3 is the module knot based on bayonet to the identifying system of the fake license plate vehicle of running time of another embodiment of the present invention
Structure schematic diagram.
Fig. 4 is the modular structure schematic diagram of the fake-licensed car capture terminal in Fig. 3 of another embodiment of the present invention.
Fig. 5 is the modular structure schematic diagram of the data acquisition module in Fig. 3 of another embodiment of the present invention.
Fig. 6 is the modular structure schematic diagram of the data cleansing module in Fig. 3 of another embodiment of the present invention.
Fig. 7 is the modular structure schematic diagram of the statistical analysis module in Fig. 3 of another embodiment of the present invention.
Fig. 8 is the identification of the fake license plate vehicle for carrying out deep learning to running time based on bayonet of another embodiment of the present invention
The flow diagram of method.
Fig. 9 is the sub-process schematic diagram of the step S800 in Fig. 8 of another embodiment of the present invention.
Figure 10 is the knowledge of the fake license plate vehicle for carrying out deep learning to running time based on bayonet of another embodiment of the present invention
The modular structure schematic diagram of other system.
Figure 11 is the modular structure schematic diagram of the deep learning module in Figure 10 of another embodiment of the present invention.
Drawing reference numeral explanation:
11, controller；12, data acquisition module；13, data cleansing module；14, statistical analysis module；17, fake-licensed car is caught
Catch terminal；121, number plate acquisition unit；122, bayonet data acquisition unit；123, bayonet longitude and latitude acquisition unit；131, data
Selecting unit；132, data cleansing unit；133, Date Conversion Unit；141, bayonet models running time threshold matrix single
Member；142, one-dimensional bayonet is to running time matrix modeling unit；143, recognition unit is statisticallyd analyze；171, hot spot bayonet obtains single
Member；172, number plate acquisition unit；173, capture unit；21, controller；22, data acquisition module；23, data cleansing module；
25, deep learning module；27, fake-licensed car captures terminal；251, bayonet is to running time threshold matrix modeling unit；252, one-dimensional
Bayonet is to running time matrix modeling unit；253, sample selecting unit；254, deep learning unit.
Specific embodiment
The embodiment of the present invention is described in detail below in conjunction with attached drawing, but the present invention can be limited by following and
The multitude of different ways of covering is implemented.
As shown in Figure 1, the preferred embodiment of the present invention provides a kind of knowledge based on bayonet to the fake license plate vehicle of running time
Other method, for effectively being identified to fake license plate vehicle.The recognition methods of the fake license plate vehicle the following steps are included:
Step S1: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S2: bayonet is established to two-dimensional matrix Km, specifically, the enlightening karr product K*K of bayonet set K itself is established, thoroughly
Nearly all bayonets form the bayonet of n*n to two-dimensional matrix K to combinationm=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S3: vehicle pass-through bayonet is established to record and obtains the adjacent bayonet of number plate to track sets Ka, specifically, obtain
Take the running track of each car, and all bayonets that the bayonet track data of each car is passed through according to running time sequencing
Sequence, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTTwo bayonet groups that middle front and back passes through
At bayonet to obtain number plate bayonet to track sets Kd, then from number plate bayonet to track sets KdMiddle extraction each car driving rail
Bayonet pair in mark forms vehicle pass-through bayonet to record, and by number plate bayonet track sets KTMiddle front and back is adjacent to be passed sequentially through
Two bayonets composition bayonet to (ki, kj) to obtain the adjacent bayonet of number plate to track sets Ka；
Step S4: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S5: bayonet is established to running time threshold value two-dimensional matrix Kn；
Step S6: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S7: identifying the number plate of fake license plate vehicle and the number plate of doubtful fake license plate vehicle, specifically, counts each number plate
Bayonet is to running time matrix KbThere is 0 probability P to running time and using normal distribution in middle bayonet, obtains the side of ± 3 σ of u
Boundary's P value, the number plate except ± 3 section σ u are fake-licensed car；Within ± 3 section σ u, the number plate near the P value of boundary is doubtful deck
Vehicle.
It is appreciated that in the step S1, the bayonet number is obtained in the traffic block port system.
It is appreciated that bayonet obtained in the step S2 is to two-dimensional matrix KmFor
It is appreciated that being acquisition vehicle bayonet data, vehicle bayonet number from traffic block port system in the step S3
Vehicle operation is generated according to including at least number plate of vehicle, bayonet number, by time, bayonet photo, and according to vehicle bayonet data
Track database.A K for example, vehicle successively passes through A, C, F, B ...iBayonet, then the number plate bayonet track sets K of the vehicleT==
{ A, C, F, B ... ki, the number plate bayonet track sets KTIt numbers, and the direction of implicit vehicle driving, that is, blocks including bayonet
Mouth direction；Then, by number plate bayonet track sets KTTwo bayonets composition bayonet that middle front and back passes through is to (ki, kj) and the number of obtaining
Board bayonet is to track sets Kd={ (A, A), (A, C), (A, B), (C, B), (C, F), (F, B) ... (ki, kj), wherein (A, A)
It represents vehicle to turn around at A bayonet, (A, C) represents vehicle and drive towards C bayonet from A bayonet, and (A, B) represents vehicle from A card
Mouth drives towards B bayonet ... ...；Again from number plate bayonet to track sets KdThe middle bayonet pair extracted in each car wheelpath, composition
Vehicle pass-through bayonet is to record, and bayonet is to (ki, kj) indicate that vehicle first passes through bayonet ki, pass through bayonet k next timej, format is such as
Under:
| Bayonet ki | Bayonet kj | By bayonet kiTime | By bayonet kjTime | License plate number |
Finally, by number plate bayonet track sets KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki,
kj), to obtain the adjacent bayonet of number plate to track sets Ka={ (A, C), (C, F), (F, B) ... (ki, kj)}.Wherein, number plate card
Mouth track sets KTFor the bayonet that vehicle continues through, therefore bayonet is to track sets KdIn bayonet in geographical location and card
It is adjacent on mouth direction, and and kiAdjacent bayonet is usually at 4 or so.
It is further appreciated that in the step S3, can also be extracted according to date range T, type of vehicle, operation type
Corresponding vehicle runs bayonet track data, while concealing other type of vehicle, the running track data of other periods, certainly may be used
The running track data of a variety of type of vehicle of simultaneous selection.Furthermore it is also possible to filter out the number of those incomplete data, mistake
According to duplicate data, and the inconsistent information of the vehicle running track data of extraction can be converted, be allowed to unified.
It is appreciated that the step S4 specifically:
According to the vehicle pass-through bayonet in step S3 to record, by vehicle by bayonet to (ki, kj) in bayonet kiTime
It is denoted as tki, by bayonet kjTime be denoted as tkj, then bayonet is to (ki, kj) journey time be tij=tkj- tki, format is such as
Under:
| Bayonet ki | Bayonet kj | By the time of bayonet ki | By the time of bayonet kj | Running time tij | License plate number |
To bayonet to (ki, kj) all running time tijUsing normal distribution, the t of u-3 σ is takenijAs bayonet kiTo card
Mouth kjMinimum time threshold value t (ki, kj)；Or when by outlier algorithm or clustering algorithm finding the driving of each bayonet pair
Between tijOutlier threshold value, outlier threshold value is bayonet kiTo bayonet kjMinimum time threshold value t (ki, kj)。
It is appreciated that in the step S5 specifically:
When initial, by bayonet to two-dimensional matrix KmIn bayonet to (ki, kj) it is assigned a value of 0, then by bayonet to two-dimensional matrix
KmIn corresponding bayonet to (ki, kj) its bayonet is substituted for running time minimum time threshold value t (ki, kj), to obtain bayonet pair
Running time threshold value two-dimensional matrix Kn, KnAs follows
It is appreciated that due to number plate bayonet track sets KTFor the bayonet that vehicle continues through, therefore number plate bayonet is to rail
Mark sequence KdThe running time of the adjacent adjacent bayonet passed sequentially through in middle front and back is most short, and the time of non-adjacent bayonet is longer, due to
Sample size is larger, even if there are bayonet damage, accidentally bat, data cleansing, deck etc. influence the factor of running time, to minimum time
The influence very little of threshold value, so that it is guaranteed that recognition result is very accurate.
It is appreciated that the step S6 is specially
By the adjacent bayonet of number plate to track sets KaBayonet to being substituted for corresponding bayonet to running time tij, then,
By the adjacent bayonet of number plate to track sets KaIn bayonet to search bayonet to running time threshold value two-dimensional matrix KnIn time
Threshold value, if bayonet is to running time tijLess than running time threshold value two-dimensional matrix KnIn time threshold be then set to 0, otherwise
Constant, the adjacent bayonet of number plate is to track sets KaBecome bayonet to running time matrix Kb, for example, Kb=t (A, C), t (C, F),
0……t(ki, kj), wherein being less than running time threshold value two-dimensional matrix K to running time from F bayonet to the bayonet of B bayonetnIn
Time threshold, therefore t (F, B) is set to 0.
It is appreciated that preferably, the recognition methods of the fake license plate vehicle is further comprising the steps of:
Step S8: being stored in fake-licensed car database for the number plate of the fake license plate vehicle of identification and the number plate of doubtful fake license plate vehicle,
Fake-licensed car is arrested for subsequent.
It is appreciated that preferably, the recognition methods of the fake license plate vehicle is further comprising the steps of:
Step S9: fake license plate vehicle is captured.
As shown in Figure 2, wherein the step S9 is specifically included:
Step S91: obtaining fake-licensed car or doubtful fake-licensed car interior for a period of time bayonet data and bayonet longitude and latitude, then right
The bayonet moving position of every trolley is clustered, and extracts cluster medium-high frequency bayonet and high frequency period as hot spot bayonet；
Step S92: obtaining number plate of vehicle to vehicle photographic, or be manually entered number plate of vehicle, or access card oral instructions in real time enter
Number plate；And
Step S93: the number plate of vehicle of acquisition is compared with fake-licensed car database in hot spot bayonet, if comparison result is
Fake-licensed car, outputting alarm information immediately, while recalling from special bus pipe data system the information of the affiliated vehicle of the number plate, auxiliary
Traffic police arrests fake license plate vehicle.
Recognition methods based on bayonet to the fake license plate vehicle of running time of the invention, by running bayonet track to vehicle
Data are for statistical analysis, data modeling, have good recognition accuracy, especially combine the register information of number plate vehicle,
Strong improve checks and detains fake-licensed car accuracy and efficiency, beautifies vehicles operation environment, ensures people's life and property safety.In addition,
The continuous feature for obtaining track of vehicle in the case where road network, range data is not needed, while can be compatible with leakage beat of data yet, is compared logical
It crosses calculating distance or determines that track continuity effect is more preferable according to road network, complexity is lower.
It is appreciated that as shown in figure 3, another embodiment of the present invention also provides a kind of set based on bayonet to running time
The identifying system of board vehicle is preferably applied to the identification side as described above based on bayonet to the fake license plate vehicle of running time
Method, the identifying system include
Data acquisition module 12 carries out the required basic data of fake license plate vehicle identification for obtaining,
Statistical analysis module 14, for for statistical analysis to vehicle running track data and identify fake license plate vehicle；
Controller 11 acquires basic data and for controlling the statistical analysis for controlling the data acquisition module 12
Module 14 is for statistical analysis and identifies fake license plate vehicle；
The controller 11 is connect with the data acquisition module 12 and statistical analysis module 14 respectively, the data acquisition
Module 12 and statistical analysis module 14 connect.
It is appreciated that preferably, the identifying system further includes
Fake-licensed car captures terminal 17, connect with the controller 11, for capturing to fake license plate vehicle.Wherein, described
Controller 11 is also used to push deck information of vehicles and captures terminal 17 to fake-licensed car.
As shown in figure 4, the fake-licensed car capture terminal 17 includes
Hot spot bayonet acquiring unit 171, for obtain fake-licensed car or doubtful fake-licensed car for a period of time in bayonet data and
Bayonet longitude and latitude, and high frequency bayonet and high frequency period are therefrom extracted as hot spot bayonet；
Number plate acquisition unit 172, for obtaining number plate of vehicle to vehicle photographic or for being manually entered number plate of vehicle, or use
In the number plate that real-time access card oral instructions enter；
Capture unit 173, for the number plate of vehicle of acquisition to be compared with fake-licensed car database in hot spot bayonet, if than
It is fake-licensed car to result, outputting alarm information immediately, while the affiliated vehicle of the number plate is recalled from special bus pipe data system
Information, auxiliary traffic police arrest fake license plate vehicle.
As shown in figure 5, the data acquisition module 12 includes
Number plate acquisition unit 121, for obtaining number plate of vehicle data, number plate of vehicle data from special bus pipe data system
Including at least number plate of vehicle, type of vehicle, operation type etc., and generate number plate of vehicle database；
Bayonet data acquisition unit 122, for obtaining vehicle bayonet data, vehicle bayonet data from traffic block port system
It numbers, by time, bayonet photo including at least number plate of vehicle, bayonet, and generates vehicle running track database；
Bayonet longitude and latitude acquisition unit 123, for obtaining the geographic position data of bayonet, bayonet from traffic block port system
Geographic position data include at least bayonet number, bayonet belonging positions region, bayonet longitude and latitude, and generate bayonet longitude and latitude degree
According to library.
It is appreciated that preferably, the identifying system further includes
Data cleansing module 13 connect with controller 11, data acquisition module 12 and statistical analysis module 14 respectively, is used for
According to date range T, type of vehicle, operation type, extracts corresponding vehicle and run bayonet track data, while concealing other types
Vehicle, the running track data of other periods, certainly can also a variety of type of vehicle of simultaneous selection running track data, filter out
Those incomplete data, the data of mistake and duplicate data, and different the writing of the vehicle running track data to extraction
Breath is converted, and is allowed to unified.
As shown in fig. 6, the data cleansing module 13 includes
Data selection unit 131, for extracting corresponding vehicle operation according to date range T, type of vehicle, operation type
Bayonet track data, while concealing other type of vehicle, the running track data of other periods, certainly can also simultaneous selection it is a variety of
The running track data of type of vehicle.
Data cleansing unit 132, for filtering out those incomplete data, the data of mistake and duplicate data；
Date Conversion Unit 133, the inconsistent information for the vehicle running track data to extraction are converted, are allowed to
It is unified.
As shown in fig. 7, the statistical analysis module 14 includes
Bayonet is exhaustive all for establishing the enlightening karr product of bayonet itself to running time threshold matrix modeling unit 141
Bayonet pair, and big data analysis is carried out to running time to each bayonet, bayonet is obtained to running time minimum threshold, forms card
Mouth is to running time threshold matrix；
One-dimensional bayonet to running time matrix modeling unit 142, for by the track data of each number plate according to time sequence, shape
At the adjacent bayonet of number plate to track sets, bayonet is searched to the corresponding threshold value of running time threshold matrix, establishes the one of each number plate
Bayonet is tieed up to running time matrix；
Statistically analyze recognition unit 143, by each number plate bayonet to bayonet in the matrix of track to the area of running time
Between the probability that is distributed carry out big data analysis, identify fake-licensed car.
It is of the invention based on bayonet to the identifying system of the fake license plate vehicle of running time, by running bayonet track to vehicle
Data are for statistical analysis, data modeling, have good recognition accuracy, especially combine the register information of number plate vehicle,
Strong improve checks and detains fake-licensed car accuracy and efficiency, beautifies vehicles operation environment, ensures people's life and property safety.In addition,
The continuous feature for obtaining track of vehicle in the case where road network, range data is not needed, while can be compatible with leakage beat of data yet, is compared logical
It crosses calculating distance or determines that track continuity effect is more preferable according to road network, complexity is lower.
It is appreciated that another embodiment of the present invention also provides a kind of computer-readable storage medium, for storing base
In the computer program that bayonet identifies running time to fake license plate vehicle, which holds when running on computers
Row following steps:
Step S1: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S2: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the card of n*n to combination
Mouth is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S3: the running track of each car is obtained, and the bayonet track data of each car is successive according to running time
All bayonets sequence that sequence is passed through, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTIn before
The two bayonets composition bayonet passed through afterwards is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track sequence
Arrange KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track sequence
Arrange KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sets
Ka；
Step S4: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S5: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S6: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S7: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet occurs 0 probability P to running time
And normal distribution is applied, the boundary P value of ± 3 σ of u is obtained, the number plate except ± 3 section σ u is fake-licensed car；Within ± 3 section σ u,
Number plate near the P value of boundary is doubtful fake-licensed car.
The form of general computer-readable medium includes: floppy disk (floppy disk), flexible disc (flexible
Disk), hard disk, tape, it is any its with magnetic medium, CD-ROM, remaining any optical medium, punched card (punch
Cards), paper tape (paper tape), remaining any physical medium of pattern with hole, random access memory (RAM),
Programmable read only memory (PROM), erasable programmable read-only memory (EPROM), the read-only storage of quick flashing erasable programmable
Device (FLASH-EPROM), remaining any memory chip or cassette or it is any remaining can allow computer read medium.Instruction
It can further be sent or receive by a transmission medium.This term of transmission medium may include any tangible or invisible medium,
It, which can be used to store, encodes or carries, is used to the instruction that executes to machine, and include digital or analog communication signal or its with
Promote the intangible medium of the communication of above-metioned instruction.Transmission medium includes coaxial cable, copper wire and optical fiber, and it comprises be used to pass
The conducting wire of the bus of a defeated computer data signal.
As shown in figure 8, another embodiment of the present invention, which also provides one kind, carries out deep learning to running time based on bayonet
Fake license plate vehicle recognition methods, comprising the following steps:
Step S100: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S200: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form n*n's to combination
Bayonet is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S300: the running track of each car is obtained, and by the bayonet track data of each car according to running time elder generation
All bayonets sequence that sequence is passed through afterwards, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTIn
Two bayonets composition bayonet that front and back passes through is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track
Sequence KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track
Sequence KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sequence
Arrange Ka；
Step S400: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S500: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S600: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S700: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet is general to running time appearance 0
Rate P simultaneously applies normal distribution, obtains the corresponding P of u valueuValue；And
Step S800: deep learning is carried out to realize intelligent recognition fake license plate vehicle using convolutional neural networks.
It is appreciated that the step S1-S6's in the content and above preferred embodiment of the step S100-S600 of the present embodiment
Content one-to-one correspondence is identical, and the difference of the two is only that step S700 and step S800.
It is appreciated that as shown in figure 9, the step S800 specifically includes the following steps:
Step S801: selection PuIt is worth the one-dimensional bayonet of x number plates nearby to running time matrix KbAs positive sample E+；
Step S802: by all number plate random combine Cheng Yitai new cars, generating new interim number plate, while merging the two card
Mouth track data, repeats step S300-S700, chooses PuIt is worth the one-dimensional bayonet of x number plates nearby to running time matrix KbAs
Negative sample E-；
Step S803: unified positive sample E+, negative sample E-With one-dimensional bayonet to running time matrix KbLength and returned
One change processing；
Step S804: by positive sample E+With negative sample E-Convolutional neural networks are inputted, carry out feature learning, and utilize
Softmax classifier classifies to the feature that convolutional network learns, and obtains the fake license plate vehicle intelligence based on one-dimensional deep learning
It can identification model RAI, then use verify data, constantly adjustment threshold value, the date, x and x value interval, sample length, convolution
Neural network parameter is trained, and obtains best fake license plate vehicle intelligent recognition model RAI；And
Step S805: best fake license plate vehicle intelligent recognition model R is utilizedAITo the one-dimensional bayonet of all vehicles to running time square
Battle array KbIt is identified, obtains deck number plate set, and be stored in fake-licensed car database.
It is appreciated that in the step S802, negative sample E-Select the bayonet rail for the deck number plate discovered and seized in history
Mark data, sample is more true, fake license plate vehicle intelligent recognition model RAIRecognition effect it is more preferable.
It is appreciated that in the step S803, since one-dimensional bayonet is to running time matrix Kb, positive sample E+Interior, negative sample
This E-The length of its interior track pair is different, needs uniformly to be adjusted to the matrix that length is L.
It is further appreciated that the identification of the fake license plate vehicle for carrying out deep learning to running time based on bayonet in the present embodiment
Method can with the recognition methods of the fake license plate vehicle of running time is applied simultaneously based on bayonet in above preferred embodiment, respectively
Recognition result is complementary to one another, and is mutually proved, and recognition accuracy is improved.
The recognition methods of the fake license plate vehicle for carrying out deep learning to running time based on bayonet of the present embodiment, by vehicle
Operation bayonet track data is for statistical analysis, and data modeling, and learnt and trained using convolutional neural networks is adopted
With convolutional neural networks, explicit feature extraction is avoided, the error introduced in threshold value setting, and implicitly from training data
Learnt；What convolutional neural networks had unique superiority, especially an one-dimensional vector in terms of image procossing can be direct
Input this feature of network avoids the complexity of data reconstruction in feature extraction and assorting process.In addition, it is not necessary that road network, away from
Obtain the continuous feature of track of vehicle in the case where from data, while can also be compatible with leakage beat of data, compare by calculate distance or
Person determines that track continuity effect is more preferable according to road network, and complexity is lower.
It is appreciated that as shown in Figure 10, another embodiment of the present invention also provide it is a kind of based on bayonet to running time into
The identifying system of the fake license plate vehicle of row deep learning is preferably applied to be based on bayonet as described above to running time progress deeply
Spend the recognition methods of the fake license plate vehicle of study.The identification of the fake license plate vehicle for carrying out deep learning to running time based on bayonet
System includes
Data acquisition module 22 carries out the required basic data of fake license plate vehicle identification for obtaining；
Deep learning module 25, for vehicle running track data carry out data modeling, statistical analysis, deep learning and
Identify fake license plate vehicle；
Controller 21 acquires basic data and for controlling the deep learning for controlling the data acquisition module 22
Module 25 carries out data modeling, statistical analysis, deep learning and identification fake license plate vehicle；
The controller 21 is connect with the data acquisition module 22 and deep learning module 25 respectively, the deep learning
Module 25 is connect with data acquisition module 22.
It is appreciated that preferably, the identifying system further includes that fake-licensed car captures terminal 27 and data cleansing module
23, the fake-licensed car captures terminal 27 and connect with controller 21, and the data cleansing module 23 is clear with controller 21, data respectively
Mold cleaning block 22 and deep learning module 25 connect.Data acquisition module 22 in the present embodiment is adopted with the data in above-described embodiment
Collection module 12 is identical, and data cleansing module 23 is identical as data cleansing module 13, and fake-licensed car captures terminal 27 and fake-licensed car captures
Terminal 17 is identical, and controller 21 is identical as controller 11.
As shown in figure 11, the deep learning module 25 includes
Bayonet is exhaustive all for establishing the enlightening karr product of bayonet itself to running time threshold matrix modeling unit 251
Bayonet pair, and big data analysis is carried out to running time to each bayonet, bayonet is obtained to running time minimum threshold, forms card
Mouth is to running time threshold matrix；
One-dimensional bayonet to running time matrix modeling unit 252, for by the track data of each number plate according to time sequence, shape
At the adjacent bayonet of number plate to track sets, bayonet is searched to the corresponding threshold value of running time threshold matrix, establishes the one of each number plate
Bayonet is tieed up to running time matrix；
Sample selecting unit 253, for the one-dimensional bayonet from a certain number of normal vehicles of selection to running time matrix
Positive sample of the data as deep learning；By all number plate random combines at a trolley, raw new interim number plate, while merging two
Person's bayonet track data obtains new one-dimensional bayonet to running time matrix Kb, choose one-dimensional bayonet identical with positive sample number
Negative sample to track matrix data as deep learning；
Data modeling unit 254, due to different by the quantity of bayonet in each vehicle fortune track data, positive sample,
Negative sample is different with the length of the one-dimensional track matrix of all number plates, and data modeling unit 254 is constructed for being drawn on the strong points to offset the weaknesses
It can input in deep learning unit 255, the one-dimensional bayonet of the matrix data of convolutional neural networks can be inputted to running time square
Battle array Kb, positive sample and negative sample, and be normalized；
Deep learning unit 255 is used for by carrying out feature learning to fixed-size positive sample and negative sample, and utilizes
Softmax classifier classifies to the feature that convolutional network learns, and obtains intelligent recognition model RAI, then, use verifying
Data, constantly adjustment T, convolutional neural networks parameter, are trained, obtain best intelligent recognition model RAI；Then, using best
Intelligent recognition model RAITo all one-dimensional bayonets to track matrix KbIt is identified, obtains deck number plate set A.
Wherein, the bayonet builds running time threshold matrix modeling unit 251 and bayonet to running time threshold matrix
Form unit 141 is identical, and one-dimensional bayonet models running time matrix modeling unit 252 and one-dimensional bayonet to running time matrix single
Member 142 is identical.
The identifying system of the fake license plate vehicle for carrying out deep learning to running time based on bayonet of the present embodiment, by vehicle
Operation bayonet track data is for statistical analysis, and data modeling, and learnt and trained using convolutional neural networks is adopted
With convolutional neural networks, explicit feature extraction is avoided, the error introduced in threshold value setting, and implicitly from training data
Learnt；What convolutional neural networks had unique superiority, especially an one-dimensional vector in terms of image procossing can be direct
Input this feature of network avoids the complexity of data reconstruction in feature extraction and assorting process.In addition, it is not necessary that road network, away from
Obtain the continuous feature of track of vehicle in the case where from data, while can also be compatible with leakage beat of data, compare by calculate distance or
Person determines that track continuity effect is more preferable according to road network, and complexity is lower.
It is appreciated that another embodiment of the present invention also provides a kind of computer-readable storage medium, for storing
Computer program of the deep learning to be identified to fake license plate vehicle is carried out to running time based on bayonet, which exists
Following steps are executed when running on computer:
Step S100: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S200: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form n*n's to combination
Bayonet is to two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S300: the running track of each car is obtained, and by the bayonet track data of each car according to running time elder generation
All bayonets sequence that sequence is passed through afterwards, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTIn
Two bayonets composition bayonet that front and back passes through is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track
Sequence KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track
Sequence KTAdjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sequence
Arrange Ka；
Step S400: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S500: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S600: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S700: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet is general to running time appearance 0
Rate P simultaneously applies normal distribution, obtains the corresponding P of u valueuValue；And
Step S800: deep learning is carried out to realize intelligent recognition fake license plate vehicle using convolutional neural networks.
The form of general computer-readable medium includes: floppy disk (floppy disk), flexible disc (flexible
Disk), hard disk, tape, it is any its with magnetic medium, CD-ROM, remaining any optical medium, punched card (punch
Cards), paper tape (paper tape), remaining any physical medium of pattern with hole, random access memory (RAM),
Programmable read only memory (PROM), erasable programmable read-only memory (EPROM), the read-only storage of quick flashing erasable programmable
Device (FLASH-EPROM), remaining any memory chip or cassette or it is any remaining can allow computer read medium.Instruction
It can further be sent or receive by a transmission medium.This term of transmission medium may include any tangible or invisible medium,
It, which can be used to store, encodes or carries, is used to the instruction that executes to machine, and include digital or analog communication signal or its with
Promote the intangible medium of the communication of above-metioned instruction.Transmission medium includes coaxial cable, copper wire and optical fiber, and it comprises be used to pass
The conducting wire of the bus of a defeated computer data signal.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (13)
1. a kind of recognition methods based on bayonet to the fake license plate vehicle of running time, which is characterized in that
The following steps are included:
Step S1: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S2: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the bayonet pair of n*n to combination
Two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S3: the running track of each car is obtained, and by the bayonet track data of each car according to running time sequencing
All bayonets sequence passed through, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTMiddle front and back is logical
The two bayonets composition bayonet crossed is to obtain number plate bayonet to track sets Kd, then from number plate bayonet to track sets KdIn mention
It takes the bayonet pair in each car wheelpath, forms vehicle pass-through bayonet to record, and by number plate bayonet track sets KTIn before
Adjacent two passed sequentially through bayonet composition bayonet is to (k afterwardsi, kj) to obtain the adjacent bayonet of number plate to track sets Ka；
Step S4: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S5: bayonet is established to running time threshold value two to running time minimum threshold to running time and bayonet based on bayonet
Tie up matrix Kn；
Step S6: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S7: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet to running time occur 0 probability P and answer
With normal distribution, the boundary P value of ± 3 σ of u is obtained, the number plate except ± 3 section σ u is fake-licensed car；Within ± 3 section σ u and on side
Number plate near boundary's P value is doubtful fake-licensed car.
2. the recognition methods of fake license plate vehicle as described in claim 1, which is characterized in that
The step S4 is specially
According to the vehicle pass-through bayonet in step S3 to record, by vehicle by bayonet to (ki, kj) in bayonet kiTime be denoted as
tki, by bayonet kjTime be denoted as tkj, then bayonet is to (ki, kj) journey time be tij=tkj- tki；
To bayonet to (ki, kj) all running time tijUsing normal distribution, the t of u-3 σ is takenijAs bayonet kiTo bayonet kj
Minimum time threshold value t (ki, kj)；Or the running time t of each bayonet pair is found by outlier algorithm or clustering algorithmij
Outlier threshold value, outlier threshold value is bayonet kiTo bayonet kjMinimum time threshold value t (ki, kj)。
3. the recognition methods of fake license plate vehicle as claimed in claim 2, which is characterized in that
The step S5 specifically:
When initial, by bayonet to two-dimensional matrix KmIn bayonet to (ki, kj) it is assigned a value of 0, then by bayonet to two-dimensional matrix KmIn
Corresponding bayonet is to (ki, kj) its bayonet is substituted for running time minimum time threshold value t (ki, kj), to obtain bayonet to driving
Time threshold two-dimensional matrix Kn。
4. the recognition methods of fake license plate vehicle as claimed in claim 3, which is characterized in that
The step S6 specifically:
By the adjacent bayonet of number plate to track sets KaBayonet to being substituted for corresponding bayonet to running time tij, then, pass through
The adjacent bayonet of number plate is to track sets KaIn bayonet to search bayonet to running time threshold value two-dimensional matrix KnIn time threshold
Value, if bayonet is to running time tijLess than KnMiddle time threshold is then set to 0, and otherwise constant, the adjacent bayonet of number plate is to track
Sequence KaBecome bayonet to running time matrix Kb。
5. the recognition methods of fake license plate vehicle as described in claim 1, which is characterized in that
The recognition methods of the fake license plate vehicle is further comprising the steps of:
Step S8: the number plate of the fake license plate vehicle of identification and the number plate of doubtful fake license plate vehicle are stored in fake-licensed car database.
6. the recognition methods of fake license plate vehicle as claimed in claim 5, which is characterized in that
The recognition methods of the fake license plate vehicle is further comprising the steps of:
Step S9: fake license plate vehicle is captured；
The step S9 includes:
Step S91: fake-licensed car or doubtful fake-licensed car interior for a period of time bayonet data and bayonet longitude and latitude are obtained, and are therefrom extracted
High frequency bayonet and high frequency period are as hot spot bayonet；
Step S92: number plate of vehicle is obtained to vehicle photographic, or is manually entered number plate of vehicle, or number that access card oral instructions in real time enter
Board；And
Step S93: the number plate of vehicle of acquisition being compared with fake-licensed car database in hot spot bayonet, if comparison result is deck
Vehicle, outputting alarm information immediately, while the information of the affiliated vehicle of the number plate is recalled from special bus pipe data system, assist traffic police
Arrest fake license plate vehicle.
7. it is a kind of based on bayonet to the identifying system of the fake license plate vehicle of running time, be suitable for such as any one of claim 1-6 institute
The recognition methods stated, which is characterized in that
Including
Data acquisition module (12) carries out the required basic data of fake license plate vehicle identification for obtaining,
Statistical analysis module (14), for for statistical analysis to vehicle running track data and identify fake license plate vehicle；
Controller (11), for controlling the data acquisition module (12) acquisition basic data and for controlling the statistical analysis
Module (14) is for statistical analysis and identifies fake license plate vehicle；
The controller (11) connect with the data acquisition module (12) and statistical analysis module (14) respectively, and the data are adopted
Collect module (12) and statistical analysis module (14) connection.
8. as claimed in claim 7 based on bayonet to the identifying system of the fake license plate vehicle of running time, which is characterized in that
The identifying system further includes
Fake-licensed car captures terminal (17), connect with the controller (11), for capturing to fake license plate vehicle；
The fake-licensed car captures terminal (17)
Hot spot bayonet acquiring unit (171), for obtaining fake-licensed car or doubtful fake-licensed car interior for a period of time bayonet data and card
Mouth longitude and latitude, and high frequency bayonet and high frequency period are therefrom extracted as hot spot bayonet；
Number plate acquisition unit (172) for obtaining number plate of vehicle to vehicle photographic or for being manually entered number plate of vehicle, or is used for
The number plate that real-time access card oral instructions enter；
Capture unit (173), for the number plate of vehicle of acquisition to be compared with fake-licensed car database in hot spot bayonet, if comparing
It as a result is fake-licensed car, outputting alarm information immediately, while recalling from special bus pipe data system the letter of the affiliated vehicle of the number plate
Breath, auxiliary traffic police arrest fake license plate vehicle.
9. a kind of computer-readable storage medium, identifies running time to fake license plate vehicle based on bayonet for storing
Computer program, which is characterized in that the computer program executes following steps when running on computers:
Step S1: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S2: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the bayonet pair of n*n to combination
Two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S3: the running track of each car is obtained, and by the bayonet track data of each car according to running time sequencing
All bayonets sequence passed through, obtains the number plate bayonet track sets K of each carT, by number plate bayonet track sets KTMiddle front and back is logical
The two bayonets composition bayonet crossed is to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track sets Kd
The middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track sets KT
Adjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sets Ka；
Step S4: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S5: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S6: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S7: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet to running time occur 0 probability P and answer
With normal distribution, the boundary P value of ± 3 σ of u is obtained, the number plate except ± 3 section σ u is fake-licensed car；Within ± 3 section σ u, boundary P
Number plate near value is doubtful fake-licensed car.
10. a kind of recognition methods for the fake license plate vehicle for carrying out deep learning to running time based on bayonet, which is characterized in that
The following steps are included:
Step S100: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S200: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the bayonet of n*n to combination
To two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S300: the running track of each car is obtained, and the bayonet track data of each car is successively suitable according to running time
All bayonets sequence that sequence is passed through, obtains the number plate bayonet track sets K of each carT, by front and back in number plate bayonet track sets
By two bayonets form bayonet to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track sets
KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track sets KT
Adjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sets Ka；
Step S400: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S500: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S600: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S700: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet to running time occur 0 probability P simultaneously
Using normal distribution, the corresponding P of u value is obtaineduValue；And
Step S800: deep learning is carried out to realize intelligent recognition fake license plate vehicle using convolutional neural networks.
11. the recognition methods of fake license plate vehicle as claimed in claim 10, which is characterized in that
The step S800 specifically includes the following steps:
Step S801: selection PuIt is worth the one-dimensional bayonet of x number plates nearby to running time matrix KbAs positive sample E+；
Step S802: by all number plate random combine Cheng Yitai new cars, generating new interim number plate, while merging the two bayonet rail
Mark data repeat step S300-S700, choose PuIt is worth the one-dimensional bayonet of x number plates nearby to running time matrix KbAs negative sample
This E-；
Step S803: unified positive sample E+, negative sample E-With one-dimensional bayonet to running time matrix KbLength and be normalized
Processing；
Step S804: by positive sample E+With negative sample E-Convolutional neural networks are inputted, carry out feature learning, and utilize softmax points
Class device classifies to the feature that convolutional network learns, and obtains the fake license plate vehicle intelligent recognition model based on one-dimensional deep learning
RAI, then use verify data, the constantly value interval of adjustment threshold value, date, x and x, sample length, convolutional neural networks ginseng
Number, is trained, obtains best fake license plate vehicle intelligent recognition model RAI；And
Step S805: best fake license plate vehicle intelligent recognition model R is utilizedAITo the one-dimensional bayonet of all vehicles to running time matrix Kb
It is identified, obtains deck number plate set.
12. a kind of identifying system for the fake license plate vehicle for carrying out deep learning to running time based on bayonet, which is characterized in that
Including
Data acquisition module (22) carries out the required basic data of fake license plate vehicle identification for obtaining,
Deep learning module (25), for carrying out data modeling, statistical analysis, deep learning and knowledge to vehicle running track data
Other fake license plate vehicle；
Controller (21), for controlling the data acquisition module (22) acquisition basic data and for controlling the deep learning
Module (25) carries out data modeling, statistical analysis, deep learning and identification fake license plate vehicle；
The controller (21) connect with the data acquisition module (22) and deep learning module (25) respectively, the depth
Module (25) are practised to connect with data acquisition module (22).
13. a kind of computer-readable storage medium carries out deep learning to running time based on bayonet for storing with right
The computer program that fake license plate vehicle is identified, which is characterized in that executed when the computer program is run on computers following
Step:
Step S100: all bayonet numbers are obtained, bayonet set K={ k is formed1, k2, k3, k4……kn}；
Step S200: establishing the enlightening karr product K*K of bayonet set K itself, and poor nearly all bayonets form the bayonet of n*n to combination
To two-dimensional matrix Km=[ki*kj], i ∈ N+∧ i≤n, j ∈ N+∧j≤n；
Step S300: the running track of each car is obtained, and the bayonet track data of each car is successively suitable according to running time
All bayonets sequence that sequence is passed through, obtains the number plate bayonet track sets K of each carT, by front and back in number plate bayonet track sets
By two bayonets form bayonet to obtain number plate bayonet to track sets Kd, then again from number plate bayonet to track sets
KdThe middle bayonet pair extracted in each car wheelpath forms vehicle pass-through bayonet to record, and by number plate bayonet track sets KT
Adjacent two passed sequentially through the bayonet composition bayonet in middle front and back is to (ki, kj) to obtain the adjacent bayonet of number plate to track sets Ka；
Step S400: statistics bayonet is to running time and bayonet to running time minimum threshold；
Step S500: bayonet is established to running time threshold value two-dimensional matrix K to running time based on bayonetn；
Step S600: it carries out one-dimensional bayonet and running time matrix is modeled to obtain bayonet to running time matrix Kb；And
Step S700: the bayonet of each number plate is counted to running time matrix KbMiddle bayonet to running time occur 0 probability P simultaneously
Using normal distribution, the corresponding P of u value is obtaineduValue；And
Step S800: deep learning is carried out to realize intelligent recognition fake license plate vehicle using convolutional neural networks.
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